Orbital Insight is a geospatial big data company leveraging the rapidly growing availability of satellite, UAV and other geospatial data sources to understand and characterize socioeconomic trends at global, regional and hyper-local scales. Orbital Insight uses recent advances in deep learning and cloud computing to process and understand the information available in millions of images. In this talk, we explore three sets of technical challenges we’ve had to overcome to develop a robust solution. First, we’ll discuss challenges associated with fusing data that can come from different types of image sensors as well as different ground truth measurement sources. Next, we’ll explore challenges related to detecting trends and analyzing changes in imagery over time. Finally, we’ll examine challenges involved in choosing the right machine learning methods for the task at hand.
Challenges and Approaches for Extracting Meaning from Satellite Imagery
Session Speakers
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Adam Kraft
Deep Learning Engineer, Orbital Insight
Adam Kraft comes from Orbital Insight, where he works on large scale computer vision and machine learning. Orbital Insight is a Geospatial big data company leveraging the rapidly growing availability of satellite, UAV and other geospatial data sources, to understand and characterize socio-economic trends at global, regional and hyper-local scales. Orbital Insight uses recent advances in deep learning and cloud computing to process and understand the information available in millions of images. Prior to Orbital Insight, Adam worked at another big data company, Amazon, creating computer vision solutions to search for millions of products by pointing your camera phone.
